Wildland Fire Mid-story: A generative modeling approach for representative fuels

TL;DR

This paper introduces a generative modeling approach to characterize mid-story fuels in wildland fires, leveraging terrestrial LiDAR data for calibration to improve fire behavior predictions.

Contribution

It presents a novel parameterized generative model for populating mid-story fuels, calibrated with LiDAR data, to enhance fire modeling accuracy.

Findings

The model effectively captures spatial density and heterogeneity of fuels.

Calibration with LiDAR data improves the realism of fuel distributions.

Code implementation is provided for reproducibility.

Abstract

Computational models for understanding and predicting fire in wildland and managed lands are increasing in impact. Data characterizing the fuels and environment is needed to continue improvement in the fidelity and reliability of fire outcomes. This paper addresses a gap in the characterization and population of mid-story fuels, which are not easily observable either through traditional survey, where data collection is time consuming, or with remote sensing, where the mid-story is typically obscured by forest canopy. We present a methodology to address populating a mid-story using a generative model for fuel placement that captures key concepts of spatial density and heterogeneity that varies by regional or local environmental conditions. The advantage of using a parameterized generative model is the ability to calibrate (or `tune') the generated fuels based on comparison to limited observation datasets or with expert guidance, and we show how this generative model can balance information from these sources to capture the essential characteristics of the wildland fuels environment. In this paper we emphasize the connection of terrestrial LiDAR (TLS) as the observations used to calibrate of the generative model, as TLS is a promising method for supporting forest fuels assessment. Code for the methods in this paper is available.